An Enhanced Deep Learning Approach to Potential Purchaser Prediction: AutoGluon Ensembles for Cross-Industry Profit Maximization

Abstract

Accurately identifying potential purchasers is critical for maximizing profitability in highly competitive markets, spanning industries from finance and insurance to telecommunications. This article presents an enhanced deep learning approach for potential purchaser prediction, leveraging an AutoGluon ensemble framework to optimize accuracy and profitability across diverse datasets, including time deposits, health insurance, 5G packages, and credit cards. The proposed AutoGluon-based ensemble integrates neural networks with boosted trees, stacking, and bagging to maximize the Expected Maximum Profit Criterion (EMPC) and deliver consistent predictive performance across datasets. Our model demonstrates superior performance in terms of Area Under the Curve (AUC), EMPC, and top decile lift (TDL) relative to benchmark classifiers. Specifically, for the credit card dataset, the model achieved an AUC of 0.8856, an EMPC of 13.8453, and a TDL of 3.80, marking significant improvements over prior results. Bayesian A/B testing, based on 40 EMPC ranks, further confirms the robustness of our model, with a 98.5% probability of being the best-performing model across datasets. The AutoGluon ensemble consistently outperforms traditional ensemble models, achieving an average rank-adjusted p-value below 0.015 in the Holm post-hoc test, validating its statistical significance. This study underscores the efficacy of deep learning ensembles in cross-industry potential purchaser prediction, providing a scalable, profit-driven approach for enhanced marketing and customer acquisition strategies.